Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis

A new approach in museum exhibitions is being explored, focusing on emotion-based displays designed to engage visitors by eliciting emotional responses to artworks. This method aims to democratize access to art and attract a more diverse audience by utilizing a continuous bi-dimensional emotion space for visual emotion analysis.

The development of a predictive tool for emotional responses to art is significant for curators, as it can streamline the process of designing exhibitions and reduce the labor-intensive task of manual annotation, which often carries the risk of curator bias.

This initiative reflects a broader trend in the integration of artificial intelligence and deep learning into various fields, including the arts, where technology is increasingly being used to enhance human experiences and interactions, as seen in other studies focusing on multimodal fusion and deepfake detection in visual media.